Overview

Dataset statistics

Number of variables22
Number of observations2351
Missing cells2020
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory487.0 KiB
Average record size in memory212.1 B

Variable types

Categorical2
Numeric20

Alerts

Country has a high cardinality: 193 distinct valuesHigh cardinality
Life Expectancy is highly overall correlated with Adult Mortality and 12 other fieldsHigh correlation
Adult Mortality is highly overall correlated with Life Expectancy and 2 other fieldsHigh correlation
Infant Deaths is highly overall correlated with Life Expectancy and 5 other fieldsHigh correlation
Alcohol is highly overall correlated with Income Composition Of Resources and 2 other fieldsHigh correlation
Percentage Expenditure is highly overall correlated with GDP and 1 other fieldsHigh correlation
Hepatitis B is highly overall correlated with Polio and 1 other fieldsHigh correlation
Measles is highly overall correlated with Infant Deaths and 1 other fieldsHigh correlation
BMI is highly overall correlated with Life Expectancy and 5 other fieldsHigh correlation
Under-Five Deaths is highly overall correlated with Life Expectancy and 6 other fieldsHigh correlation
Polio is highly overall correlated with Life Expectancy and 4 other fieldsHigh correlation
Diphtheria is highly overall correlated with Life Expectancy and 4 other fieldsHigh correlation
HIV/AIDS is highly overall correlated with Life Expectancy and 5 other fieldsHigh correlation
GDP is highly overall correlated with Life Expectancy and 5 other fieldsHigh correlation
Thinness 10-19 Years is highly overall correlated with Life Expectancy and 4 other fieldsHigh correlation
Thinness 5-9 Years is highly overall correlated with Life Expectancy and 4 other fieldsHigh correlation
Income Composition Of Resources is highly overall correlated with Life Expectancy and 14 other fieldsHigh correlation
Schooling is highly overall correlated with Life Expectancy and 12 other fieldsHigh correlation
Status is highly overall correlated with Life Expectancy and 3 other fieldsHigh correlation
Alcohol has 159 (6.8%) missing valuesMissing
Hepatitis B has 433 (18.4%) missing valuesMissing
BMI has 29 (1.2%) missing valuesMissing
Total Expenditure has 180 (7.7%) missing valuesMissing
GDP has 348 (14.8%) missing valuesMissing
Population has 513 (21.8%) missing valuesMissing
Thinness 10-19 Years has 29 (1.2%) missing valuesMissing
Thinness 5-9 Years has 29 (1.2%) missing valuesMissing
Income Composition Of Resources has 127 (5.4%) missing valuesMissing
Schooling has 123 (5.2%) missing valuesMissing
Infant Deaths has 682 (29.0%) zerosZeros
Percentage Expenditure has 479 (20.4%) zerosZeros
Measles has 801 (34.1%) zerosZeros
Under-Five Deaths has 638 (27.1%) zerosZeros
Income Composition Of Resources has 111 (4.7%) zerosZeros
Schooling has 24 (1.0%) zerosZeros

Reproduction

Analysis started2023-01-20 04:34:56.557667
Analysis finished2023-01-20 04:35:45.797173
Duration49.24 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Country
Categorical

Distinct193
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size101.3 KiB
Switzerland
 
16
Antigua and Barbuda
 
16
Slovakia
 
16
Benin
 
16
Bahamas
 
15
Other values (188)
2272 

Length

Max length52
Median length34
Mean length10.012335
Min length4

Characters and Unicode

Total characters23539
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.4%

Sample

1st rowBahamas
2nd rowHungary
3rd rowFrance
4th rowGambia
5th rowQatar

Common Values

ValueCountFrequency (%)
Switzerland 16
 
0.7%
Antigua and Barbuda 16
 
0.7%
Slovakia 16
 
0.7%
Benin 16
 
0.7%
Bahamas 15
 
0.6%
Iran (Islamic Republic of) 15
 
0.6%
Zambia 15
 
0.6%
South Africa 15
 
0.6%
Mexico 15
 
0.6%
Cameroon 15
 
0.6%
Other values (183) 2197
93.4%

Length

2023-01-19T23:35:45.890795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic 150
 
4.4%
of 146
 
4.3%
and 81
 
2.4%
united 50
 
1.5%
guinea 39
 
1.1%
the 36
 
1.1%
democratic 34
 
1.0%
new 29
 
0.8%
south 28
 
0.8%
saint 28
 
0.8%
Other values (223) 2803
81.9%

Most occurring characters

ValueCountFrequency (%)
a 3397
14.4%
i 1996
 
8.5%
e 1719
 
7.3%
n 1718
 
7.3%
r 1281
 
5.4%
o 1278
 
5.4%
1073
 
4.6%
u 899
 
3.8%
l 884
 
3.8%
t 871
 
3.7%
Other values (46) 8423
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19128
81.3%
Uppercase Letter 3173
 
13.5%
Space Separator 1073
 
4.6%
Open Punctuation 52
 
0.2%
Close Punctuation 52
 
0.2%
Other Punctuation 37
 
0.2%
Dash Punctuation 24
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3397
17.8%
i 1996
10.4%
e 1719
 
9.0%
n 1718
 
9.0%
r 1281
 
6.7%
o 1278
 
6.7%
u 899
 
4.7%
l 884
 
4.6%
t 871
 
4.6%
d 709
 
3.7%
Other values (17) 4376
22.9%
Uppercase Letter
ValueCountFrequency (%)
S 384
 
12.1%
B 274
 
8.6%
C 224
 
7.1%
M 224
 
7.1%
A 211
 
6.6%
R 193
 
6.1%
G 182
 
5.7%
T 164
 
5.2%
I 153
 
4.8%
L 150
 
4.7%
Other values (14) 1014
32.0%
Space Separator
ValueCountFrequency (%)
1073
100.0%
Open Punctuation
ValueCountFrequency (%)
( 52
100.0%
Close Punctuation
ValueCountFrequency (%)
) 52
100.0%
Other Punctuation
ValueCountFrequency (%)
' 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22301
94.7%
Common 1238
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3397
15.2%
i 1996
 
9.0%
e 1719
 
7.7%
n 1718
 
7.7%
r 1281
 
5.7%
o 1278
 
5.7%
u 899
 
4.0%
l 884
 
4.0%
t 871
 
3.9%
d 709
 
3.2%
Other values (41) 7549
33.9%
Common
ValueCountFrequency (%)
1073
86.7%
( 52
 
4.2%
) 52
 
4.2%
' 37
 
3.0%
- 24
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23526
99.9%
None 13
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3397
14.4%
i 1996
 
8.5%
e 1719
 
7.3%
n 1718
 
7.3%
r 1281
 
5.4%
o 1278
 
5.4%
1073
 
4.6%
u 899
 
3.8%
l 884
 
3.8%
t 871
 
3.7%
Other values (45) 8410
35.7%
None
ValueCountFrequency (%)
ô 13
100.0%

Year
Real number (ℝ)

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.5653
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:46.043637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12004
median2008
Q32012
95-th percentile2015
Maximum2015
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6241608
Coefficient of variation (CV)0.0023033676
Kurtosis-1.2277667
Mean2007.5653
Median Absolute Deviation (MAD)4
Skewness-0.015535921
Sum4719786
Variance21.382863
MonotonicityNot monotonic
2023-01-19T23:35:46.177973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2012 158
 
6.7%
2013 156
 
6.6%
2011 154
 
6.6%
2005 153
 
6.5%
2004 150
 
6.4%
2015 150
 
6.4%
2002 147
 
6.3%
2014 146
 
6.2%
2001 146
 
6.2%
2006 146
 
6.2%
Other values (6) 845
35.9%
ValueCountFrequency (%)
2000 142
6.0%
2001 146
6.2%
2002 147
6.3%
2003 142
6.0%
2004 150
6.4%
2005 153
6.5%
2006 146
6.2%
2007 139
5.9%
2008 140
6.0%
2009 146
6.2%
ValueCountFrequency (%)
2015 150
6.4%
2014 146
6.2%
2013 156
6.6%
2012 158
6.7%
2011 154
6.6%
2010 136
5.8%
2009 146
6.2%
2008 140
6.0%
2007 139
5.9%
2006 146
6.2%

Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size101.3 KiB
Developing
1944 
Developed
407 

Length

Max length10
Median length10
Mean length9.8268822
Min length9

Characters and Unicode

Total characters23103
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeveloping
2nd rowDeveloped
3rd rowDeveloping
4th rowDeveloping
5th rowDeveloping

Common Values

ValueCountFrequency (%)
Developing 1944
82.7%
Developed 407
 
17.3%

Length

2023-01-19T23:35:46.371609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-19T23:35:46.500676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
developing 1944
82.7%
developed 407
 
17.3%

Most occurring characters

ValueCountFrequency (%)
e 5109
22.1%
D 2351
10.2%
v 2351
10.2%
l 2351
10.2%
o 2351
10.2%
p 2351
10.2%
i 1944
 
8.4%
n 1944
 
8.4%
g 1944
 
8.4%
d 407
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20752
89.8%
Uppercase Letter 2351
 
10.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5109
24.6%
v 2351
11.3%
l 2351
11.3%
o 2351
11.3%
p 2351
11.3%
i 1944
 
9.4%
n 1944
 
9.4%
g 1944
 
9.4%
d 407
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
D 2351
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23103
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5109
22.1%
D 2351
10.2%
v 2351
10.2%
l 2351
10.2%
o 2351
10.2%
p 2351
10.2%
i 1944
 
8.4%
n 1944
 
8.4%
g 1944
 
8.4%
d 407
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5109
22.1%
D 2351
10.2%
v 2351
10.2%
l 2351
10.2%
o 2351
10.2%
p 2351
10.2%
i 1944
 
8.4%
n 1944
 
8.4%
g 1944
 
8.4%
d 407
 
1.8%

Life Expectancy
Real number (ℝ)

Distinct359
Distinct (%)15.3%
Missing10
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean69.171807
Minimum36.3
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:47.714231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum36.3
5-th percentile51.4
Q163
median72.1
Q375.7
95-th percentile81.9
Maximum89
Range52.7
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation9.5302326
Coefficient of variation (CV)0.13777626
Kurtosis-0.23497696
Mean69.171807
Median Absolute Deviation (MAD)5.7
Skewness-0.651593
Sum161931.2
Variance90.825333
MonotonicityNot monotonic
2023-01-19T23:35:47.851652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 39
 
1.7%
75 26
 
1.1%
78 24
 
1.0%
76 23
 
1.0%
74.5 22
 
0.9%
81 21
 
0.9%
73.6 21
 
0.9%
74.1 21
 
0.9%
73.8 20
 
0.9%
75.4 20
 
0.9%
Other values (349) 2104
89.5%
ValueCountFrequency (%)
36.3 1
< 0.1%
39 1
< 0.1%
41 1
< 0.1%
41.5 1
< 0.1%
42.3 1
< 0.1%
43.1 1
< 0.1%
43.3 1
< 0.1%
43.5 1
< 0.1%
43.8 1
< 0.1%
44 1
< 0.1%
ValueCountFrequency (%)
89 8
0.3%
88 6
0.3%
87 5
0.2%
86 12
0.5%
85 10
0.4%
84 9
0.4%
83.7 1
 
< 0.1%
83.5 2
 
0.1%
83.4 1
 
< 0.1%
83.3 1
 
< 0.1%

Adult Mortality
Real number (ℝ)

Distinct402
Distinct (%)17.2%
Missing10
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean165.19393
Minimum1
Maximum717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:47.994570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q174
median144
Q3228
95-th percentile397
Maximum717
Range716
Interquartile range (IQR)154

Descriptive statistics

Standard deviation124.46646
Coefficient of variation (CV)0.75345663
Kurtosis1.685638
Mean165.19393
Median Absolute Deviation (MAD)76
Skewness1.156974
Sum386719
Variance15491.901
MonotonicityNot monotonic
2023-01-19T23:35:48.151562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 26
 
1.1%
16 25
 
1.1%
14 22
 
0.9%
11 21
 
0.9%
138 20
 
0.9%
19 20
 
0.9%
13 18
 
0.8%
15 18
 
0.8%
17 18
 
0.8%
189 17
 
0.7%
Other values (392) 2136
90.9%
ValueCountFrequency (%)
1 12
0.5%
2 6
 
0.3%
3 6
 
0.3%
4 3
 
0.1%
5 2
 
0.1%
6 12
0.5%
7 14
0.6%
8 10
0.4%
9 6
 
0.3%
11 21
0.9%
ValueCountFrequency (%)
717 1
< 0.1%
715 1
< 0.1%
699 1
< 0.1%
686 1
< 0.1%
682 1
< 0.1%
679 1
< 0.1%
675 1
< 0.1%
666 1
< 0.1%
665 1
< 0.1%
652 1
< 0.1%

Infant Deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct187
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.023394
Minimum0
Maximum1800
Zeros682
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:48.324423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q323
95-th percentile91.5
Maximum1800
Range1800
Interquartile range (IQR)23

Descriptive statistics

Standard deviation115.91805
Coefficient of variation (CV)3.8609243
Kurtosis117.88201
Mean30.023394
Median Absolute Deviation (MAD)3
Skewness9.8645077
Sum70585
Variance13436.995
MonotonicityNot monotonic
2023-01-19T23:35:48.493919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 682
29.0%
1 278
 
11.8%
2 150
 
6.4%
3 139
 
5.9%
4 77
 
3.3%
8 44
 
1.9%
7 44
 
1.9%
9 37
 
1.6%
6 36
 
1.5%
10 35
 
1.5%
Other values (177) 829
35.3%
ValueCountFrequency (%)
0 682
29.0%
1 278
11.8%
2 150
 
6.4%
3 139
 
5.9%
4 77
 
3.3%
5 30
 
1.3%
6 36
 
1.5%
7 44
 
1.9%
8 44
 
1.9%
9 37
 
1.6%
ValueCountFrequency (%)
1800 1
< 0.1%
1700 2
0.1%
1600 1
< 0.1%
1500 1
< 0.1%
1400 1
< 0.1%
1300 2
0.1%
1200 1
< 0.1%
1100 2
0.1%
910 1
< 0.1%
576 1
< 0.1%

Alcohol
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct987
Distinct (%)45.0%
Missing159
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean4.627208
Minimum0.01
Maximum17.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:48.642886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.8575
median3.81
Q37.8125
95-th percentile11.89
Maximum17.87
Range17.86
Interquartile range (IQR)6.955

Descriptive statistics

Standard deviation4.0557091
Coefficient of variation (CV)0.87649164
Kurtosis-0.79987762
Mean4.627208
Median Absolute Deviation (MAD)3.3
Skewness0.57603928
Sum10142.84
Variance16.448777
MonotonicityNot monotonic
2023-01-19T23:35:48.772651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 236
 
10.0%
0.03 12
 
0.5%
0.02 11
 
0.5%
0.04 10
 
0.4%
1.18 10
 
0.4%
0.09 9
 
0.4%
0.55 9
 
0.4%
0.21 8
 
0.3%
0.56 8
 
0.3%
0.17 7
 
0.3%
Other values (977) 1872
79.6%
(Missing) 159
 
6.8%
ValueCountFrequency (%)
0.01 236
10.0%
0.02 11
 
0.5%
0.03 12
 
0.5%
0.04 10
 
0.4%
0.05 6
 
0.3%
0.06 7
 
0.3%
0.07 4
 
0.2%
0.08 5
 
0.2%
0.09 9
 
0.4%
0.1 6
 
0.3%
ValueCountFrequency (%)
17.87 1
< 0.1%
17.31 1
< 0.1%
16.99 1
< 0.1%
16.35 1
< 0.1%
15.52 1
< 0.1%
15.19 1
< 0.1%
15.14 1
< 0.1%
15.07 1
< 0.1%
15.04 2
0.1%
14.97 1
< 0.1%

Percentage Expenditure
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1873
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean747.5649
Minimum0
Maximum19479.912
Zeros479
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:48.944882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.0839692
median68.866233
Q3451.18919
95-th percentile4577.459
Maximum19479.912
Range19479.912
Interquartile range (IQR)446.10522

Descriptive statistics

Standard deviation2018.8211
Coefficient of variation (CV)2.7005295
Kurtosis27.401783
Mean747.5649
Median Absolute Deviation (MAD)68.866233
Skewness4.7166897
Sum1757525.1
Variance4075638.5
MonotonicityNot monotonic
2023-01-19T23:35:49.095627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 479
 
20.4%
460.6487805 1
 
< 0.1%
229.6687488 1
 
< 0.1%
12829.25408 1
 
< 0.1%
2.995959823 1
 
< 0.1%
173.5755595 1
 
< 0.1%
21.24915339 1
 
< 0.1%
77.27092119 1
 
< 0.1%
59.55033546 1
 
< 0.1%
530.4832336 1
 
< 0.1%
Other values (1863) 1863
79.2%
ValueCountFrequency (%)
0 479
20.4%
0.09987219 1
 
< 0.1%
0.328418056 1
 
< 0.1%
0.358651421 1
 
< 0.1%
0.388253772 1
 
< 0.1%
0.397228764 1
 
< 0.1%
0.442802404 1
 
< 0.1%
0.5305728 1
 
< 0.1%
0.661540371 1
 
< 0.1%
0.66751505 1
 
< 0.1%
ValueCountFrequency (%)
19479.91161 1
< 0.1%
19099.04506 1
< 0.1%
18961.3486 1
< 0.1%
18822.86732 1
< 0.1%
18379.32974 1
< 0.1%
17028.52798 1
< 0.1%
16255.16198 1
< 0.1%
15515.75234 1
< 0.1%
15345.4907 1
< 0.1%
14714.82588 1
< 0.1%

Hepatitis B
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct86
Distinct (%)4.5%
Missing433
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean80.69708
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:49.233366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q177
median92
Q397
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.342835
Coefficient of variation (CV)0.31404896
Kurtosis2.5931835
Mean80.69708
Median Absolute Deviation (MAD)6
Skewness-1.8953133
Sum154777
Variance642.25926
MonotonicityNot monotonic
2023-01-19T23:35:49.426920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 195
 
8.3%
98 177
 
7.5%
96 132
 
5.6%
97 121
 
5.1%
95 118
 
5.0%
94 92
 
3.9%
93 82
 
3.5%
92 70
 
3.0%
91 61
 
2.6%
88 57
 
2.4%
Other values (76) 813
34.6%
(Missing) 433
18.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
 
0.1%
4 4
 
0.2%
5 6
 
0.3%
6 13
 
0.6%
7 17
 
0.7%
8 35
1.5%
9 51
2.2%
11 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
99 195
8.3%
98 177
7.5%
97 121
5.1%
96 132
5.6%
95 118
5.0%
94 92
3.9%
93 82
3.5%
92 70
 
3.0%
91 61
 
2.6%
89 53
 
2.3%

Measles
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct816
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2341.4232
Minimum0
Maximum212183
Zeros801
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:49.596719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q3389
95-th percentile9639
Maximum212183
Range212183
Interquartile range (IQR)389

Descriptive statistics

Standard deviation11501.395
Coefficient of variation (CV)4.9121383
Kurtosis131.66781
Mean2341.4232
Median Absolute Deviation (MAD)17
Skewness10.193184
Sum5504686
Variance1.3228208 × 108
MonotonicityNot monotonic
2023-01-19T23:35:49.743906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 801
34.1%
1 77
 
3.3%
2 53
 
2.3%
3 32
 
1.4%
6 26
 
1.1%
4 24
 
1.0%
7 20
 
0.9%
8 20
 
0.9%
10 17
 
0.7%
15 17
 
0.7%
Other values (806) 1264
53.8%
ValueCountFrequency (%)
0 801
34.1%
1 77
 
3.3%
2 53
 
2.3%
3 32
 
1.4%
4 24
 
1.0%
5 15
 
0.6%
6 26
 
1.1%
7 20
 
0.9%
8 20
 
0.9%
9 15
 
0.6%
ValueCountFrequency (%)
212183 1
< 0.1%
182485 1
< 0.1%
168107 1
< 0.1%
141258 1
< 0.1%
133802 1
< 0.1%
131441 1
< 0.1%
124219 1
< 0.1%
118712 1
< 0.1%
109023 1
< 0.1%
90387 1
< 0.1%

BMI
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct585
Distinct (%)25.2%
Missing29
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean38.301378
Minimum1
Maximum87.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:49.883960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.3
Q119.4
median43.2
Q355.9
95-th percentile64.5
Maximum87.3
Range86.3
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation19.886979
Coefficient of variation (CV)0.51922359
Kurtosis-1.2807274
Mean38.301378
Median Absolute Deviation (MAD)16.3
Skewness-0.21354484
Sum88935.8
Variance395.49194
MonotonicityNot monotonic
2023-01-19T23:35:50.019937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.5 16
 
0.7%
55.8 14
 
0.6%
54.2 13
 
0.6%
6.9 12
 
0.5%
59.9 12
 
0.5%
56.1 11
 
0.5%
54.1 11
 
0.5%
52.8 11
 
0.5%
57 11
 
0.5%
55 11
 
0.5%
Other values (575) 2200
93.6%
(Missing) 29
 
1.2%
ValueCountFrequency (%)
1 1
 
< 0.1%
1.4 2
 
0.1%
1.9 1
 
< 0.1%
2 1
 
< 0.1%
2.1 9
0.4%
2.2 8
0.3%
2.3 4
0.2%
2.4 4
0.2%
2.5 8
0.3%
2.6 3
 
0.1%
ValueCountFrequency (%)
87.3 1
< 0.1%
83.3 1
< 0.1%
82.8 1
< 0.1%
81.6 1
< 0.1%
79.3 1
< 0.1%
77.6 1
< 0.1%
77.3 1
< 0.1%
76.2 1
< 0.1%
75.7 1
< 0.1%
75.2 2
0.1%

Under-Five Deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct226
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.724373
Minimum0
Maximum2400
Zeros638
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:50.163448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q329.5
95-th percentile131.5
Maximum2400
Range2400
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation157.56431
Coefficient of variation (CV)3.7763134
Kurtosis109.29505
Mean41.724373
Median Absolute Deviation (MAD)4
Skewness9.4953203
Sum98094
Variance24826.51
MonotonicityNot monotonic
2023-01-19T23:35:50.290610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 638
27.1%
1 283
 
12.0%
4 128
 
5.4%
2 126
 
5.4%
3 100
 
4.3%
8 39
 
1.7%
12 38
 
1.6%
5 37
 
1.6%
10 35
 
1.5%
6 35
 
1.5%
Other values (216) 892
37.9%
ValueCountFrequency (%)
0 638
27.1%
1 283
12.0%
2 126
 
5.4%
3 100
 
4.3%
4 128
 
5.4%
5 37
 
1.6%
6 35
 
1.5%
7 23
 
1.0%
8 39
 
1.7%
9 31
 
1.3%
ValueCountFrequency (%)
2400 1
< 0.1%
2300 1
< 0.1%
2200 1
< 0.1%
2100 1
< 0.1%
2000 1
< 0.1%
1900 1
< 0.1%
1800 1
< 0.1%
1700 1
< 0.1%
1600 1
< 0.1%
1500 1
< 0.1%

Polio
Real number (ℝ)

Distinct71
Distinct (%)3.0%
Missing15
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.566781
Minimum3
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:50.444365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range96
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.351954
Coefficient of variation (CV)0.28282505
Kurtosis3.8152315
Mean82.566781
Median Absolute Deviation (MAD)6
Skewness-2.1001803
Sum192876
Variance545.31374
MonotonicityNot monotonic
2023-01-19T23:35:50.592841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 303
 
12.9%
98 212
 
9.0%
97 169
 
7.2%
96 151
 
6.4%
95 142
 
6.0%
94 124
 
5.3%
93 98
 
4.2%
92 80
 
3.4%
91 63
 
2.7%
88 57
 
2.4%
Other values (61) 937
39.9%
ValueCountFrequency (%)
3 5
 
0.2%
4 10
 
0.4%
5 5
 
0.2%
6 11
 
0.5%
7 21
0.9%
8 33
1.4%
9 51
2.2%
17 1
 
< 0.1%
23 1
 
< 0.1%
26 3
 
0.1%
ValueCountFrequency (%)
99 303
12.9%
98 212
9.0%
97 169
7.2%
96 151
6.4%
95 142
6.0%
94 124
5.3%
93 98
 
4.2%
92 80
 
3.4%
91 63
 
2.7%
89 42
 
1.8%

Total Expenditure
Real number (ℝ)

Distinct771
Distinct (%)35.5%
Missing180
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean5.9169784
Minimum0.37
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:50.743642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.93
Q14.27
median5.73
Q37.46
95-th percentile9.74
Maximum17.6
Range17.23
Interquartile range (IQR)3.19

Descriptive statistics

Standard deviation2.4658485
Coefficient of variation (CV)0.41674118
Kurtosis1.1353685
Mean5.9169784
Median Absolute Deviation (MAD)1.57
Skewness0.59369317
Sum12845.76
Variance6.0804089
MonotonicityNot monotonic
2023-01-19T23:35:50.877715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6 12
 
0.5%
6.7 11
 
0.5%
5.6 9
 
0.4%
5.92 9
 
0.4%
5.3 9
 
0.4%
4.2 9
 
0.4%
4.34 8
 
0.3%
3.7 8
 
0.3%
5.26 8
 
0.3%
4.48 8
 
0.3%
Other values (761) 2080
88.5%
(Missing) 180
 
7.7%
ValueCountFrequency (%)
0.37 1
 
< 0.1%
0.65 1
 
< 0.1%
0.76 1
 
< 0.1%
0.92 1
 
< 0.1%
1.1 2
0.1%
1.12 3
0.1%
1.15 1
 
< 0.1%
1.17 2
0.1%
1.18 2
0.1%
1.19 2
0.1%
ValueCountFrequency (%)
17.6 1
< 0.1%
17.24 1
< 0.1%
17.2 1
< 0.1%
17 1
< 0.1%
16.9 1
< 0.1%
16.61 1
< 0.1%
15.57 1
< 0.1%
15.27 1
< 0.1%
15.15 1
< 0.1%
15.14 1
< 0.1%

Diphtheria
Real number (ℝ)

Distinct80
Distinct (%)3.4%
Missing15
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.280822
Minimum3
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:51.031626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range96
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.735929
Coefficient of variation (CV)0.28847463
Kurtosis3.4878016
Mean82.280822
Median Absolute Deviation (MAD)6
Skewness-2.0549042
Sum192208
Variance563.39434
MonotonicityNot monotonic
2023-01-19T23:35:51.160540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 290
 
12.3%
98 212
 
9.0%
97 170
 
7.2%
95 154
 
6.6%
96 143
 
6.1%
94 116
 
4.9%
93 100
 
4.3%
92 81
 
3.4%
91 71
 
3.0%
87 54
 
2.3%
Other values (70) 945
40.2%
ValueCountFrequency (%)
3 2
 
0.1%
4 9
 
0.4%
5 7
 
0.3%
6 14
 
0.6%
7 19
 
0.8%
8 32
1.4%
9 52
2.2%
16 1
 
< 0.1%
19 1
 
< 0.1%
21 1
 
< 0.1%
ValueCountFrequency (%)
99 290
12.3%
98 212
9.0%
97 170
7.2%
96 143
6.1%
95 154
6.6%
94 116
 
4.9%
93 100
 
4.3%
92 81
 
3.4%
91 71
 
3.0%
89 48
 
2.0%

HIV/AIDS
Real number (ℝ)

Distinct179
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7679711
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:51.300005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.8
95-th percentile8.75
Maximum50.6
Range50.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation5.152181
Coefficient of variation (CV)2.9141772
Kurtosis35.966834
Mean1.7679711
Median Absolute Deviation (MAD)0
Skewness5.4637664
Sum4156.5
Variance26.544969
MonotonicityNot monotonic
2023-01-19T23:35:51.442512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 1413
60.1%
0.2 103
 
4.4%
0.3 94
 
4.0%
0.4 57
 
2.4%
0.5 35
 
1.5%
0.6 28
 
1.2%
0.8 27
 
1.1%
0.9 22
 
0.9%
0.7 21
 
0.9%
2.1 20
 
0.9%
Other values (169) 531
 
22.6%
ValueCountFrequency (%)
0.1 1413
60.1%
0.2 103
 
4.4%
0.3 94
 
4.0%
0.4 57
 
2.4%
0.5 35
 
1.5%
0.6 28
 
1.2%
0.7 21
 
0.9%
0.8 27
 
1.1%
0.9 22
 
0.9%
1 7
 
0.3%
ValueCountFrequency (%)
50.6 1
< 0.1%
50.3 1
< 0.1%
49.9 1
< 0.1%
49.1 1
< 0.1%
48.8 1
< 0.1%
46.4 1
< 0.1%
43.7 1
< 0.1%
43.5 1
< 0.1%
42.1 1
< 0.1%
40.7 1
< 0.1%

GDP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2003
Distinct (%)100.0%
Missing348
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean7494.8422
Minimum1.68135
Maximum119172.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:51.584905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.68135
5-th percentile67.818135
Q1467.24579
median1761.5376
Q35953.1312
95-th percentile41553.124
Maximum119172.74
Range119171.06
Interquartile range (IQR)5485.8854

Descriptive statistics

Standard deviation14373.885
Coefficient of variation (CV)1.9178369
Kurtosis12.945812
Mean7494.8422
Median Absolute Deviation (MAD)1595.3059
Skewness3.2691926
Sum15012169
Variance2.0660858 × 108
MonotonicityNot monotonic
2023-01-19T23:35:51.736973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
437.4467238 1
 
< 0.1%
55.31382 1
 
< 0.1%
249.939542 1
 
< 0.1%
36526.7711 1
 
< 0.1%
4313.1682 1
 
< 0.1%
1642.837974 1
 
< 0.1%
74114.69715 1
 
< 0.1%
31.437144 1
 
< 0.1%
2377.74739 1
 
< 0.1%
165.8794176 1
 
< 0.1%
Other values (1993) 1993
84.8%
(Missing) 348
 
14.8%
ValueCountFrequency (%)
1.68135 1
< 0.1%
3.685949 1
< 0.1%
4.6135745 1
< 0.1%
5.6687264 1
< 0.1%
11.147277 1
< 0.1%
11.631377 1
< 0.1%
12.1789279 1
< 0.1%
12.566464 1
< 0.1%
12.989164 1
< 0.1%
13.154199 1
< 0.1%
ValueCountFrequency (%)
119172.7418 1
< 0.1%
115761.577 1
< 0.1%
114293.8433 1
< 0.1%
113751.85 1
< 0.1%
89739.7117 1
< 0.1%
88564.82298 1
< 0.1%
87998.44468 1
< 0.1%
86852.7119 1
< 0.1%
85814.58857 1
< 0.1%
84658.88768 1
< 0.1%

Population
Real number (ℝ)

Distinct1831
Distinct (%)99.6%
Missing513
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean11851853
Minimum34
Maximum1.1796812 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:51.880821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile8925.65
Q1199782.25
median1448220.5
Q37588384.5
95-th percentile46645199
Maximum1.1796812 × 109
Range1.1796812 × 109
Interquartile range (IQR)7388602.2

Descriptive statistics

Standard deviation52641796
Coefficient of variation (CV)4.4416512
Kurtosis357.57483
Mean11851853
Median Absolute Deviation (MAD)1422024
Skewness17.090864
Sum2.1783705 × 1010
Variance2.7711586 × 1015
MonotonicityNot monotonic
2023-01-19T23:35:52.067040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
444 3
 
0.1%
127445 2
 
0.1%
1141 2
 
0.1%
718239 2
 
0.1%
26868 2
 
0.1%
292 2
 
0.1%
4515429 1
 
< 0.1%
6473164 1
 
< 0.1%
5934232 1
 
< 0.1%
6662468 1
 
< 0.1%
Other values (1821) 1821
77.5%
(Missing) 513
 
21.8%
ValueCountFrequency (%)
34 1
< 0.1%
41 1
< 0.1%
43 1
< 0.1%
123 1
< 0.1%
135 1
< 0.1%
146 1
< 0.1%
286 1
< 0.1%
292 2
0.1%
297 1
< 0.1%
333 1
< 0.1%
ValueCountFrequency (%)
1179681239 1
< 0.1%
1144118674 1
< 0.1%
1126135777 1
< 0.1%
258162113 1
< 0.1%
236159276 1
< 0.1%
232989141 1
< 0.1%
198686688 1
< 0.1%
196796269 1
< 0.1%
194895996 1
< 0.1%
186917361 1
< 0.1%

Thinness 10-19 Years
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct191
Distinct (%)8.2%
Missing29
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.8883721
Minimum0.1
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:52.233937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.6
median3.4
Q37.2
95-th percentile14.095
Maximum27.5
Range27.4
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.4624252
Coefficient of variation (CV)0.91286528
Kurtosis3.7135964
Mean4.8883721
Median Absolute Deviation (MAD)2.4
Skewness1.6853481
Sum11350.8
Variance19.913238
MonotonicityNot monotonic
2023-01-19T23:35:52.412759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9 53
 
2.3%
2.1 53
 
2.3%
1 53
 
2.3%
2.2 50
 
2.1%
0.9 49
 
2.1%
0.8 49
 
2.1%
1.5 47
 
2.0%
2 47
 
2.0%
1.3 46
 
2.0%
1.7 45
 
1.9%
Other values (181) 1830
77.8%
ValueCountFrequency (%)
0.1 23
1.0%
0.2 31
1.3%
0.3 28
1.2%
0.4 4
 
0.2%
0.5 27
1.1%
0.6 35
1.5%
0.7 42
1.8%
0.8 49
2.1%
0.9 49
2.1%
1 53
2.3%
ValueCountFrequency (%)
27.5 1
 
< 0.1%
27.4 1
 
< 0.1%
27.3 1
 
< 0.1%
27.2 2
0.1%
27.1 1
 
< 0.1%
27 3
0.1%
26.9 2
0.1%
26.7 1
 
< 0.1%
22.2 1
 
< 0.1%
22 1
 
< 0.1%

Thinness 5-9 Years
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct200
Distinct (%)8.6%
Missing29
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.9130491
Minimum0.1
Maximum28.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:52.572155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.6
median3.4
Q37.2
95-th percentile14.1
Maximum28.5
Range28.4
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.5522915
Coefficient of variation (CV)0.92657156
Kurtosis4.0975021
Mean4.9130491
Median Absolute Deviation (MAD)2.4
Skewness1.7537464
Sum11408.1
Variance20.723358
MonotonicityNot monotonic
2023-01-19T23:35:52.716539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3 53
 
2.3%
0.9 53
 
2.3%
2.1 53
 
2.3%
1 52
 
2.2%
1.9 50
 
2.1%
1.1 46
 
2.0%
2.5 45
 
1.9%
2 45
 
1.9%
1.5 45
 
1.9%
0.5 44
 
1.9%
Other values (190) 1836
78.1%
ValueCountFrequency (%)
0.1 29
1.2%
0.2 36
1.5%
0.3 23
1.0%
0.4 16
 
0.7%
0.5 44
1.9%
0.6 43
1.8%
0.7 32
1.4%
0.8 28
1.2%
0.9 53
2.3%
1 52
2.2%
ValueCountFrequency (%)
28.5 1
< 0.1%
28.4 1
< 0.1%
28.3 1
< 0.1%
28.2 1
< 0.1%
28.1 1
< 0.1%
28 1
< 0.1%
27.9 1
< 0.1%
27.8 2
0.1%
27.7 1
< 0.1%
27.6 1
< 0.1%

Income Composition Of Resources
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct601
Distinct (%)27.0%
Missing127
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.62505081
Minimum0
Maximum0.948
Zeros111
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:52.858640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2533
Q10.49175
median0.677
Q30.77825
95-th percentile0.89
Maximum0.948
Range0.948
Interquartile range (IQR)0.2865

Descriptive statistics

Standard deviation0.21354859
Coefficient of variation (CV)0.34164997
Kurtosis1.3481375
Mean0.62505081
Median Absolute Deviation (MAD)0.1285
Skewness-1.1463804
Sum1390.113
Variance0.045602999
MonotonicityNot monotonic
2023-01-19T23:35:53.033064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 111
 
4.7%
0.7 13
 
0.6%
0.737 11
 
0.5%
0.739 11
 
0.5%
0.703 10
 
0.4%
0.723 10
 
0.4%
0.877 10
 
0.4%
0.735 10
 
0.4%
0.86 10
 
0.4%
0.636 10
 
0.4%
Other values (591) 2018
85.8%
(Missing) 127
 
5.4%
ValueCountFrequency (%)
0 111
4.7%
0.253 1
 
< 0.1%
0.255 1
 
< 0.1%
0.261 1
 
< 0.1%
0.268 2
 
0.1%
0.27 1
 
< 0.1%
0.276 1
 
< 0.1%
0.278 1
 
< 0.1%
0.279 1
 
< 0.1%
0.283 1
 
< 0.1%
ValueCountFrequency (%)
0.948 1
< 0.1%
0.945 1
< 0.1%
0.942 1
< 0.1%
0.941 1
< 0.1%
0.939 1
< 0.1%
0.938 1
< 0.1%
0.937 1
< 0.1%
0.936 2
0.1%
0.934 1
< 0.1%
0.932 2
0.1%

Schooling
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct172
Distinct (%)7.7%
Missing123
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean11.94605
Minimum0
Maximum20.7
Zeros24
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-01-19T23:35:53.163144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8
Q110
median12.4
Q314.2
95-th percentile16.8
Maximum20.7
Range20.7
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation3.385039
Coefficient of variation (CV)0.28336052
Kurtosis0.86592251
Mean11.94605
Median Absolute Deviation (MAD)2.1
Skewness-0.60558388
Sum26615.8
Variance11.458489
MonotonicityNot monotonic
2023-01-19T23:35:53.304302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.9 48
 
2.0%
13.3 39
 
1.7%
12.7 39
 
1.7%
12.5 37
 
1.6%
12.8 36
 
1.5%
12.4 35
 
1.5%
15.8 33
 
1.4%
10.7 33
 
1.4%
12.6 33
 
1.4%
12.3 32
 
1.4%
Other values (162) 1863
79.2%
(Missing) 123
 
5.2%
ValueCountFrequency (%)
0 24
1.0%
2.8 1
 
< 0.1%
2.9 3
 
0.1%
3.1 1
 
< 0.1%
3.3 1
 
< 0.1%
3.4 1
 
< 0.1%
3.5 3
 
0.1%
3.6 1
 
< 0.1%
3.7 1
 
< 0.1%
3.8 2
 
0.1%
ValueCountFrequency (%)
20.7 1
 
< 0.1%
20.6 1
 
< 0.1%
20.5 1
 
< 0.1%
20.4 2
0.1%
20.3 3
0.1%
20.1 2
0.1%
19.8 1
 
< 0.1%
19.7 1
 
< 0.1%
19.5 2
0.1%
19.3 2
0.1%

Interactions

2023-01-19T23:35:41.929205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:57.153562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:59.136091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:01.075183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:03.122026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:07.214728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:09.213342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:11.676383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:13.692821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:16.111438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:17.998018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:20.072013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:22.162203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:25.087780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:27.559827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:30.315701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:32.695896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:35.004208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:37.357215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:39.431005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:42.156652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:57.262679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:59.247689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:01.168746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:05.325438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:07.315517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:09.382164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:11.779058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:13.798066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:16.213041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:18.101624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:20.177324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:22.305454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:25.231137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:27.666427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:30.456719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:32.806456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:35.109354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:37.476973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:39.536203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:42.283751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:57.352776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:59.352634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:01.262392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:05.417293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:07.402965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:09.494795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:11.868695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:13.890635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:16.319275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:18.201168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:20.271353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:22.428148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:25.488883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:27.756882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:30.601983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:32.911423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:35.215617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:37.582683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:39.638990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:42.466514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:57.454167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:59.449948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:01.364346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:05.527210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:07.501988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:09.603152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:11.975086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:13.991044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:16.432194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:18.300720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:20.372071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:22.560667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:25.644123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:27.863936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:30.762656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:33.020235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:35.315975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:37.685712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:39.745920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:42.644233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:57.551781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:59.547834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:01.469410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:05.634462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:07.599011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:09.716624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:12.069297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:14.098701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:16.531348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:18.442184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:20.492024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:22.730010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:25.758558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:27.968467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:30.914804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:33.133895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:35.419582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:37.824138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:39.860574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:42.775632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:57.655209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:59.642565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:01.581157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:05.734305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:07.695865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:09.835131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:12.174430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:14.200009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:16.636077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:18.549338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:20.596051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:22.856506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:25.899491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:28.074449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:31.074242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:33.255466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:35.533386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:37.934391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:39.961750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:42.890632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:57.756546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:59.749612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:01.702924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:05.858208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:07.800840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:09.949947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:12.290877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:14.311708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:16.730342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:18.661587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:20.711348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:23.030563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:26.059073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:28.178146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-19T23:35:34.596536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:36.882172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:39.040773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:41.314126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:44.431162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:58.834394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:00.803600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:02.829601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:06.925111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:08.874074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:11.331323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:13.382838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:15.823433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:17.730227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:19.767259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:21.797278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:24.760462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:27.265484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:29.938907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:32.379462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:34.689980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:36.984737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:39.135972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:41.445634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:44.550108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:58.929295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:00.892447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:02.932808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:07.026419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:08.977998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:11.473273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:13.483711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:15.919959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:17.821101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:19.871845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:21.914351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:24.866163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:27.361575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:30.066922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:32.486199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:34.796053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:37.087599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:39.232143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:41.617065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:44.679418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:34:59.028167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:00.980374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:03.022722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:07.117818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:09.075246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:11.569445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:13.584985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:16.018492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:17.910544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:19.975150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:22.031507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:24.964795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:27.458990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:30.210377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:32.588991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:34.896942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:37.190801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:39.328193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-19T23:35:41.755387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-19T23:35:53.450074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
YearLife ExpectancyAdult MortalityInfant DeathsAlcoholPercentage ExpenditureHepatitis BMeaslesBMIUnder-Five DeathsPolioTotal ExpenditureDiphtheriaHIV/AIDSGDPPopulationThinness 10-19 YearsThinness 5-9 YearsIncome Composition Of ResourcesSchoolingStatus
Year1.0000.154-0.046-0.050-0.108-0.0570.110-0.0930.155-0.0500.1180.0700.138-0.0580.1740.034-0.029-0.0280.2060.1940.000
Life Expectancy0.1541.000-0.651-0.6060.4340.4330.346-0.2930.585-0.6250.5400.2890.544-0.7580.646-0.117-0.611-0.6190.8590.8110.618
Adult Mortality-0.046-0.6511.0000.397-0.223-0.300-0.2200.149-0.3870.411-0.322-0.181-0.3260.518-0.3860.1070.3890.399-0.546-0.4930.361
Infant Deaths-0.050-0.6060.3971.000-0.378-0.364-0.3430.583-0.4800.994-0.432-0.216-0.4260.491-0.5090.4640.4680.476-0.574-0.5980.056
Alcohol-0.1080.434-0.223-0.3781.0000.3080.114-0.2060.326-0.3760.2700.3260.280-0.1950.434-0.025-0.457-0.4510.5090.5430.659
Percentage Expenditure-0.0570.433-0.300-0.3640.3081.0000.107-0.1570.276-0.3660.2230.1610.230-0.2640.815-0.090-0.310-0.3120.5040.4880.447
Hepatitis B0.1100.346-0.220-0.3430.1140.1071.000-0.2280.196-0.3420.7870.0330.811-0.3420.263-0.123-0.051-0.0630.3580.3550.166
Measles-0.093-0.2930.1490.583-0.206-0.157-0.2281.000-0.2870.584-0.268-0.194-0.2670.212-0.2220.3070.3270.337-0.236-0.2830.000
BMI0.1550.585-0.387-0.4800.3260.2760.196-0.2871.000-0.4930.3320.2700.339-0.5140.475-0.076-0.571-0.5780.6150.6090.447
Under-Five Deaths-0.050-0.6250.4110.994-0.376-0.366-0.3420.584-0.4931.000-0.437-0.220-0.4310.516-0.5190.4560.4750.482-0.587-0.6110.051
Polio0.1180.540-0.322-0.4320.2700.2230.787-0.2680.332-0.4371.0000.1370.923-0.4880.417-0.109-0.235-0.2390.5370.5320.307
Total Expenditure0.0700.289-0.181-0.2160.3260.1610.033-0.1940.270-0.2200.1371.0000.154-0.1420.154-0.096-0.362-0.3780.2190.2830.414
Diphtheria0.1380.544-0.326-0.4260.2800.2300.811-0.2670.339-0.4310.9230.1541.000-0.4730.421-0.095-0.243-0.2470.5360.5330.311
HIV/AIDS-0.058-0.7580.5180.491-0.195-0.264-0.3420.212-0.5140.516-0.488-0.142-0.4731.000-0.4850.0960.4790.465-0.647-0.6190.123
GDP0.1740.646-0.386-0.5090.4340.8150.263-0.2220.475-0.5190.4170.1540.421-0.4851.000-0.067-0.425-0.4300.6940.6660.494
Population0.034-0.1170.1070.464-0.025-0.090-0.1230.307-0.0760.456-0.109-0.096-0.0950.096-0.0671.0000.1030.114-0.078-0.0870.053
Thinness 10-19 Years-0.029-0.6110.3890.468-0.457-0.310-0.0510.327-0.5710.475-0.235-0.362-0.2430.479-0.4250.1031.0000.949-0.577-0.5700.459
Thinness 5-9 Years-0.028-0.6190.3990.476-0.451-0.312-0.0630.337-0.5780.482-0.239-0.378-0.2470.465-0.4300.1140.9491.000-0.573-0.5670.462
Income Composition Of Resources0.2060.859-0.546-0.5740.5090.5040.358-0.2360.615-0.5870.5370.2190.536-0.6470.694-0.078-0.577-0.5731.0000.8970.709
Schooling0.1940.811-0.493-0.5980.5430.4880.355-0.2830.609-0.6110.5320.2830.533-0.6190.666-0.087-0.570-0.5670.8971.0000.637
Status0.0000.6180.3610.0560.6590.4470.1660.0000.4470.0510.3070.4140.3110.1230.4940.0530.4590.4620.7090.6371.000

Missing values

2023-01-19T23:35:44.927364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-19T23:35:45.232962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-19T23:35:45.502888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CountryYearStatusLife ExpectancyAdult MortalityInfant DeathsAlcoholPercentage ExpenditureHepatitis BMeaslesBMIUnder-Five DeathsPolioTotal ExpenditureDiphtheriaHIV/AIDSGDPPopulationThinness 10-19 YearsThinness 5-9 YearsIncome Composition Of ResourcesSchooling
167Bahamas2008Developing74.5167.0010.150.0000009.006.1093.07.3093.00.1NaNNaN2.52.50.79112.6
1155Hungary2014Developed75.6137.000.01160.944934NaN064.2099.07.4099.00.114117.9766809866468.01.71.60.83415.8
937France2008Developing89.088.0311.907002.78592547.060459.1398.01.5798.00.145413.6571006437499.00.60.60.87716.1
973Gambia2004Developing57.3296.032.510.00000095.002.3688.04.3587.02.9NaNNaN9.49.30.4047.6
2081Qatar2005Developing76.684.001.184582.47608197.07465.1098.03.1097.00.151488.495290NaN4.84.50.83013.7
354Brazil2013Developing74.7146.0467.24916.27084596.022054.55296.08.4897.00.112216.9446002248632.02.82.70.73414.2
2570Thailand2014Developing74.6152.086.41789.07729599.0032.41099.04.1299.00.15941.84710068416772.07.87.80.73713.6
1658Mauritania2008Developing61.4217.080.0261.76263874.0426.11273.03.2374.01.31167.535689347541.09.28.90.4757.2
790Ecuador2011Developing75.3131.073.99344.74121688.025751.4885.05.9288.00.15223.35176315177355.01.31.20.71013.2
116Australia2011Developed82.063.0110.3010986.26527092.019064.4192.09.2092.00.162245.129000223424.00.60.60.92719.8
CountryYearStatusLife ExpectancyAdult MortalityInfant DeathsAlcoholPercentage ExpenditureHepatitis BMeaslesBMIUnder-Five DeathsPolioTotal ExpenditureDiphtheriaHIV/AIDSGDPPopulationThinness 10-19 YearsThinness 5-9 YearsIncome Composition Of ResourcesSchooling
2686Turkey2010Developing74.2116.0211.4932.78235896.0761.92597.05.6197.00.11672.56930072326914.04.94.70.71513.0
2161Rwanda2005Developing55.337.0247.0139.44666795.012915.43895.06.8395.07.1287.9318778991735.06.87.00.3888.8
96Armenia2015Developing74.8118.01NaN0.00000094.03354.9196.0NaN94.00.1369.654776291695.02.12.20.74112.7
1761Morocco2002Developing69.515.0250.4666.72933992.0600046.52994.05.3194.00.11413.75717629512368.06.66.50.5408.8
1593Malaysia2008Developing73.8132.030.47438.45192197.033433.4497.03.4797.00.18513.6295412711169.08.58.30.74712.5
1147Honduras2006Developing72.8161.053.23192.92978394.0043.5694.07.6395.00.81437.628785754146.02.42.40.58310.8
2154Rwanda2012Developing64.6239.0130.019.77467698.07519.51898.07.6898.00.7678.7969731788853.06.16.10.47510.5
1766Mozambique2013Developing55.346.0621.165.81333878.0821.88778.05.9078.05.165.98568126434372.03.63.50.4059.1
1122Haiti2015Developing63.524.014NaN0.0000006.0049.91856.0NaN6.00.5814.546395171161.03.93.90.4909.1
1346Kazakhstan2015Developing72.0198.04NaN0.00000098.052653.1598.0NaN98.00.1159.98170017544126.02.42.50.79315.0